chaos-mesh VS jaeger

Compare chaos-mesh vs jaeger and see what are their differences.

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chaos-mesh jaeger
17 94
6,278 19,279
1.9% 1.4%
8.5 9.7
8 days ago about 14 hours ago
Go Go
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

chaos-mesh

Posts with mentions or reviews of chaos-mesh. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-10.
  • Building Resilience with Chaos Engineering and Litmus
    4 projects | dev.to | 10 Jun 2023
    Litmus, Gremlin, Chaos Mesh, and Chaos Monkey are all popular open-source tools used for chaos engineering. As we will be using AWS cloud infrastructure, we will also explore AWS Fault Injection Simulator (FIS). While they share the same goals of testing and improving the resilience of a system, there are some differences between them. Here are some comparisons:
  • rootly Vs firehydrant, any experience?
    2 projects | /r/sre | 28 Feb 2023
    https://chaos-mesh.org/ (open source)
  • Implement DevSecOps to Secure your CI/CD pipeline
    54 projects | dev.to | 27 Sep 2022
    Implement Chaos Mesh and Litmus chaos engineering framework to understand the behavior and stability of application in real-world use cases.
  • Chaos Mesh for chaos engineering in Kubernetes
    3 projects | /r/kubernetes | 24 Jun 2022
    Here is our recent experience with Chaos Mesh for performing basic chaos engineering experiments on an application in Kubernetes.
  • Database Mesh 2.0: Database Governance in a Cloud Native Environment
    5 projects | dev.to | 1 Jun 2022
    In March 2018, an article titled Service Mesh is the broad trend, what about Database Mesh?, was pubslished on InfoQ China and went viral in the technical community. In this article, Zhang Liang, the founder of Apache ShardingSphere, described Database Mesh concept along with the idea of Service Mesh. Four years later, the Database Mesh concept has been integrated by several companies together with their own tools and ecosystems. Today, in addition to Service Mesh, a variety of “X Mesh” concepts such as ChaosMesh, EventMesh, IOMesh have emerged. Following four years of development, Database Mesh has also started a new chapter: Database Mesh 2.0.
  • Chaos Mesh 2.0: To a Chaos Engineering Ecology
    3 projects | dev.to | 22 Aug 2021
    Thanks to all Chaos Mesh Contributors, Chaos Mesh couldn’t have come from 1.0 to 2.0 without all of your efforts!
  • Cloud Native Chaos Engineering with Chaos Mesh
    3 projects | dev.to | 9 Aug 2021
    ChaosMesh
  • Best way to determine pod resources ?
    2 projects | /r/kubernetes | 21 Apr 2021
  • Celebrating One Year of Chaos Mesh: Looking Back and Ahead
    5 projects | dev.to | 1 Apr 2021
    It's been a year since Chaos Mesh was first open-sourced on GitHub. Chaos Mesh started out as a mere fault injection tool and is now heading towards the goal of building a chaos engineering ecology. Meanwhile, the Chaos Mesh community was also built from scratch and has helped Chaos Mesh join CNCF as a Sandbox project.

jaeger

Posts with mentions or reviews of jaeger. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-01.
  • Show HN: An open source performance monitoring tool
    2 projects | news.ycombinator.com | 1 Feb 2024
    As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

    This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

    What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

    From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

    In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

    To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

    Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

    [1] https://github.com/highlight/highlight/blob/main/LICENSE

    [2] https://www.jaegertracing.io

    [3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

    [4] https://www.highlight.io/docs/getting-started/native-opentel...

    [5] https://static.highlight.io/assets/docs/gorm.png

    [6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

    [7] https://highlight.io/blog/clickhouse-materialized-views

    [8] https://www.highlight.io/docs/getting-started/self-host/self...

    [9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

  • Kubernetes Ingress Visibility
    2 projects | /r/kubernetes | 10 Dec 2023
    For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements
  • Migrating to OpenTelemetry
    8 projects | news.ycombinator.com | 16 Nov 2023
    Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

    [1] https://www.jaegertracing.io/

  • The Road to GraphQL At Enterprise Scale
    6 projects | dev.to | 8 Nov 2023
    From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.
  • OpenTelemetry Exporters - Types and Configuration Steps
    5 projects | dev.to | 30 Oct 2023
    Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.
  • Fault Tolerance in Distributed Systems: Strategies and Case Studies
    4 projects | dev.to | 18 Oct 2023
    However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.
  • Observability in Action Part 3: Enhancing Your Codebase with OpenTelemetry
    3 projects | dev.to | 17 Oct 2023
    In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.
  • Distributed Tracing and OpenTelemetry Guide
    5 projects | dev.to | 28 Sep 2023
    In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.
  • Event-Driven Architecture 101
    3 projects | dev.to | 25 Sep 2023
    For example, without investment, visibility into system behavior as a whole can be much more difficult in an event-driven system. Investing in something like Open Telemetry and a service catalog is a good idea.  Getting started with these things are relatively simple, but if you want to store your traces somewhere that are searchable, you are going to have to either pay for a SaaS tool that ingests them or you are going to have to run and maintain an open source tool capable of this such as Jaeger. For service cataloging, Backstage is becoming a very popular option.   Depending on the capabilities and the capacity of your engineering team, this might be a good option and many companies do have platform teams that provide tooling such as this. With the average salary of a platform Engineer being ~$144k, companies should think carefully on whether the benefits of an EDA are going to outweigh the cost. We will dig deeper into this in part 2 and 3 of the series.
  • The Complete Microservices Guide
    17 projects | dev.to | 21 Sep 2023
    Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

What are some alternatives?

When comparing chaos-mesh and jaeger you can also consider the following projects:

Sentry - Developer-first error tracking and performance monitoring

skywalking - APM, Application Performance Monitoring System

litmus - Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub (https://hub.litmuschaos.io). Community notes is at https://hackmd.io/a4Zu_sH4TZGeih-xCimi3Q

prometheus - The Prometheus monitoring system and time series database.

signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool

Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.

fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows

hypertrace - An open source distributed tracing & observability platform

VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database

Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

Gin - Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.

zipkin - Zipkin is a distributed tracing system